FinArg-2 is part of the NTCIR Financial Argument shared task series which aims to improve argument understanding in financial analysis. FinArg-2 aims to introduce "Temporal Inference of Financial Arguments" focusing on the assessment of temporal information, which is a distinct phenomenon in financial opinions. FTRI participates in FinArg-2 on the Earnings Conference Calls (ECC) subtask, where models must identify the temporal reference associated with an argument. At the initial stage we conducted experiments on variation of transformers models using several configurations at the preprocessing and training stages. BERT-Base-Uncased, BERT-Large-Uncased, and RoBERTa-Base-Uncased showed slightly superior performance compared to the other models. So, in the overall model that we created, we only fine-tuned those models as our baseline model. Our first model’s output FTRIECC₁, we use a transformer encoder approach with BERT-Large, resulting in 71. 43% Micro F1 and 68. 58% Macro F1. Our second model’s output FTRIECC₂, we use attention mask in Claim, Premise, and (Year + Quarter) approach with BERT-Base, resulting in 69. 05% Micro F1 and 65. 76% Macro F1. Our third model’s output FTRIECC₃, we use TF-IDF (Claim + Premise) + One-hot encoding (Year + Quarter) approach with BERT-Base, resulting in 77. 38% Micro F1 and 75. 07% Macro F1, which is the best results in this ECC Subtask. The evaluation results show that the 3 output models we created are in the top 4 among other participants based on Micro and Macro F1.
Erfina et al. (Fri,) studied this question.